Overview

Dataset statistics

Number of variables15
Number of observations249023
Missing cells89
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.5 MiB
Average record size in memory120.0 B

Variable types

Numeric9
Categorical2
DateTime2
Text2

Alerts

Unnamed: 0 is highly overall correlated with cityHigh correlation
barometer is highly overall correlated with tempHigh correlation
city is highly overall correlated with Unnamed: 0High correlation
temp is highly overall correlated with barometerHigh correlation
minute has 247670 (99.5%) zerosZeros
wind has 26711 (10.7%) zerosZeros

Reproduction

Analysis started2024-05-08 10:38:51.172988
Analysis finished2024-05-08 10:39:54.432428
Duration1 minute and 3.26 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION 

Distinct246170
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121690.69
Minimum0
Maximum246169
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2024-05-08T13:39:54.664993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9598.1
Q159402.5
median121658
Q3183913.5
95-th percentile233717.9
Maximum246169
Range246169
Interquartile range (IQR)124511

Descriptive statistics

Standard deviation71831.547
Coefficient of variation (CV)0.59027974
Kurtosis-1.2032973
Mean121690.69
Median Absolute Deviation (MAD)62256
Skewness0.0025511142
Sum3.030378 × 1010
Variance5.1597711 × 109
MonotonicityNot monotonic
2024-05-08T13:39:54.926363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
< 0.1%
1917 2
 
< 0.1%
1897 2
 
< 0.1%
1898 2
 
< 0.1%
1899 2
 
< 0.1%
1900 2
 
< 0.1%
1901 2
 
< 0.1%
1902 2
 
< 0.1%
1903 2
 
< 0.1%
1904 2
 
< 0.1%
Other values (246160) 249003
> 99.9%
ValueCountFrequency (%)
0 2
< 0.1%
1 2
< 0.1%
2 2
< 0.1%
3 2
< 0.1%
4 2
< 0.1%
5 2
< 0.1%
6 2
< 0.1%
7 2
< 0.1%
8 2
< 0.1%
9 2
< 0.1%
ValueCountFrequency (%)
246169 1
< 0.1%
246168 1
< 0.1%
246167 1
< 0.1%
246166 1
< 0.1%
246165 1
< 0.1%
246164 1
< 0.1%
246163 1
< 0.1%
246162 1
< 0.1%
246161 1
< 0.1%
246160 1
< 0.1%

city
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Jawf
20352 
Mecca
20268 
Tabuk
20240 
Northern boarder
20235 
Hail
20121 
Other values (8)
147807 

Length

Max length16
Median length6
Mean length5.7335226
Min length2

Characters and Unicode

Total characters1427779
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQassim
2nd rowQassim
3rd rowQassim
4th rowQassim
5th rowQassim

Common Values

ValueCountFrequency (%)
Jawf 20352
8.2%
Mecca 20268
8.1%
Tabuk 20240
8.1%
Northern boarder 20235
8.1%
Hail 20121
8.1%
Madina 19965
8.0%
Baha 19959
8.0%
Najran 19847
8.0%
Jazan 19829
8.0%
Qassim 19793
7.9%
Other values (3) 48414
19.4%

Length

2024-05-08T13:39:55.182703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jawf 20352
 
7.6%
mecca 20268
 
7.5%
tabuk 20240
 
7.5%
northern 20235
 
7.5%
boarder 20235
 
7.5%
hail 20121
 
7.5%
madina 19965
 
7.4%
baha 19959
 
7.4%
najran 19847
 
7.4%
jazan 19829
 
7.4%
Other values (4) 68207
25.3%

Most occurring characters

ValueCountFrequency (%)
a 296630
20.8%
r 114275
 
8.0%
i 89788
 
6.3%
n 79876
 
5.6%
s 66562
 
4.7%
e 60738
 
4.3%
d 56621
 
4.0%
h 56615
 
4.0%
c 40536
 
2.8%
b 40475
 
2.8%
Other values (23) 525663
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1427779
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 296630
20.8%
r 114275
 
8.0%
i 89788
 
6.3%
n 79876
 
5.6%
s 66562
 
4.7%
e 60738
 
4.3%
d 56621
 
4.0%
h 56615
 
4.0%
c 40536
 
2.8%
b 40475
 
2.8%
Other values (23) 525663
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1427779
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 296630
20.8%
r 114275
 
8.0%
i 89788
 
6.3%
n 79876
 
5.6%
s 66562
 
4.7%
e 60738
 
4.3%
d 56621
 
4.0%
h 56615
 
4.0%
c 40536
 
2.8%
b 40475
 
2.8%
Other values (23) 525663
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1427779
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 296630
20.8%
r 114275
 
8.0%
i 89788
 
6.3%
n 79876
 
5.6%
s 66562
 
4.7%
e 60738
 
4.3%
d 56621
 
4.0%
h 56615
 
4.0%
c 40536
 
2.8%
b 40475
 
2.8%
Other values (23) 525663
36.8%

date
Date

Distinct850
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Minimum2017-01-01 00:00:00
Maximum2019-04-30 00:00:00
2024-05-08T13:39:55.397501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:55.916654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

time
Date

Distinct710
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Minimum2024-05-08 00:00:00
Maximum2024-05-08 23:55:00
2024-05-08T13:39:56.400217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:56.824671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

year
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2017
108659 
2018
103920 
2019
36444 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters996092
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2017 108659
43.6%
2018 103920
41.7%
2019 36444
 
14.6%

Length

2024-05-08T13:39:57.188868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-08T13:39:57.521083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 108659
43.6%
2018 103920
41.7%
2019 36444
 
14.6%

Most occurring characters

ValueCountFrequency (%)
2 249023
25.0%
0 249023
25.0%
1 249023
25.0%
7 108659
10.9%
8 103920
10.4%
9 36444
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 996092
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 249023
25.0%
0 249023
25.0%
1 249023
25.0%
7 108659
10.9%
8 103920
10.4%
9 36444
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 996092
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 249023
25.0%
0 249023
25.0%
1 249023
25.0%
7 108659
10.9%
8 103920
10.4%
9 36444
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 996092
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 249023
25.0%
0 249023
25.0%
1 249023
25.0%
7 108659
10.9%
8 103920
10.4%
9 36444
 
3.7%

month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0506941
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2024-05-08T13:39:57.846137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.521591
Coefficient of variation (CV)0.58201438
Kurtosis-1.2509603
Mean6.0506941
Median Absolute Deviation (MAD)3
Skewness0.18490882
Sum1506762
Variance12.401603
MonotonicityNot monotonic
2024-05-08T13:39:58.174656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 26925
10.8%
1 26074
10.5%
4 25910
10.4%
2 23817
9.6%
10 19093
7.7%
12 18705
7.5%
11 18503
7.4%
7 18334
7.4%
8 18279
7.3%
5 18147
7.3%
Other values (2) 35236
14.1%
ValueCountFrequency (%)
1 26074
10.5%
2 23817
9.6%
3 26925
10.8%
4 25910
10.4%
5 18147
7.3%
6 17571
7.1%
7 18334
7.4%
8 18279
7.3%
9 17665
7.1%
10 19093
7.7%
ValueCountFrequency (%)
12 18705
7.5%
11 18503
7.4%
10 19093
7.7%
9 17665
7.1%
8 18279
7.3%
7 18334
7.4%
6 17571
7.1%
5 18147
7.3%
4 25910
10.4%
3 26925
10.8%

day
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.691081
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2024-05-08T13:39:58.533140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7879583
Coefficient of variation (CV)0.56006074
Kurtosis-1.1921646
Mean15.691081
Median Absolute Deviation (MAD)8
Skewness0.0096390535
Sum3907440
Variance77.228211
MonotonicityNot monotonic
2024-05-08T13:39:58.940064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 8255
 
3.3%
27 8244
 
3.3%
20 8244
 
3.3%
22 8243
 
3.3%
5 8238
 
3.3%
7 8234
 
3.3%
8 8233
 
3.3%
6 8221
 
3.3%
13 8220
 
3.3%
4 8219
 
3.3%
Other values (21) 166672
66.9%
ValueCountFrequency (%)
1 8255
3.3%
2 8211
3.3%
3 8146
3.3%
4 8219
3.3%
5 8238
3.3%
6 8221
3.3%
7 8234
3.3%
8 8233
3.3%
9 8179
3.3%
10 8136
3.3%
ValueCountFrequency (%)
31 4707
1.9%
30 7390
3.0%
29 7334
2.9%
28 8147
3.3%
27 8244
3.3%
26 8205
3.3%
25 8217
3.3%
24 8147
3.3%
23 8108
3.3%
22 8243
3.3%

hour
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.53689
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2024-05-08T13:39:59.321071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median13
Q319
95-th percentile23
Maximum24
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.9102538
Coefficient of variation (CV)0.55119361
Kurtosis-1.1978157
Mean12.53689
Median Absolute Deviation (MAD)6
Skewness-0.0045631312
Sum3121974
Variance47.751608
MonotonicityNot monotonic
2024-05-08T13:39:59.685014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
7 10479
 
4.2%
8 10478
 
4.2%
13 10471
 
4.2%
17 10457
 
4.2%
23 10454
 
4.2%
24 10454
 
4.2%
10 10439
 
4.2%
22 10437
 
4.2%
21 10433
 
4.2%
11 10429
 
4.2%
Other values (14) 144492
58.0%
ValueCountFrequency (%)
1 10363
4.2%
2 10229
4.1%
3 10033
4.0%
4 10095
4.1%
5 10265
4.1%
6 10420
4.2%
7 10479
4.2%
8 10478
4.2%
9 10415
4.2%
10 10439
4.2%
ValueCountFrequency (%)
24 10454
4.2%
23 10454
4.2%
22 10437
4.2%
21 10433
4.2%
20 10356
4.2%
19 10368
4.2%
18 10281
4.1%
17 10457
4.2%
16 10398
4.2%
15 10423
4.2%

minute
Real number (ℝ)

ZEROS 

Distinct59
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13110837
Minimum0
Maximum59
Zeros247670
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2024-05-08T13:40:00.130092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum59
Range59
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.9707102
Coefficient of variation (CV)15.031155
Kurtosis310.58921
Mean0.13110837
Median Absolute Deviation (MAD)0
Skewness16.922587
Sum32649
Variance3.8836988
MonotonicityNot monotonic
2024-05-08T13:40:00.567487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 247670
99.5%
30 112
 
< 0.1%
25 50
 
< 0.1%
15 48
 
< 0.1%
23 47
 
< 0.1%
16 45
 
< 0.1%
18 43
 
< 0.1%
24 38
 
< 0.1%
31 37
 
< 0.1%
34 35
 
< 0.1%
Other values (49) 898
 
0.4%
ValueCountFrequency (%)
0 247670
99.5%
1 9
 
< 0.1%
2 14
 
< 0.1%
3 13
 
< 0.1%
4 16
 
< 0.1%
5 24
 
< 0.1%
6 20
 
< 0.1%
7 20
 
< 0.1%
8 21
 
< 0.1%
9 19
 
< 0.1%
ValueCountFrequency (%)
59 1
 
< 0.1%
58 2
< 0.1%
57 1
 
< 0.1%
56 2
< 0.1%
55 3
< 0.1%
54 4
< 0.1%
53 2
< 0.1%
51 3
< 0.1%
50 4
< 0.1%
49 1
 
< 0.1%
Distinct81
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2024-05-08T13:40:01.057377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length6
Mean length8.5479654
Min length4

Characters and Unicode

Total characters2128640
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowClear
2nd rowClear
3rd rowClear
4th rowClear
5th rowClear
ValueCountFrequency (%)
clear 98828
31.1%
sunny 89984
28.3%
clouds 53051
16.7%
passing 35929
 
11.3%
scattered 15565
 
4.9%
partly 8757
 
2.8%
thunderstorms 2470
 
0.8%
duststorm 1897
 
0.6%
fog 1807
 
0.6%
haze 1467
 
0.5%
Other values (28) 7765
 
2.4%
2024-05-08T13:40:02.025270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
320967
15.1%
n 221542
10.4%
l 164596
 
7.7%
a 162209
 
7.6%
u 148913
 
7.0%
s 142875
 
6.7%
e 138751
 
6.5%
r 132470
 
6.2%
y 100415
 
4.7%
C 98868
 
4.6%
Other values (28) 497034
23.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2128640
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
320967
15.1%
n 221542
10.4%
l 164596
 
7.7%
a 162209
 
7.6%
u 148913
 
7.0%
s 142875
 
6.7%
e 138751
 
6.5%
r 132470
 
6.2%
y 100415
 
4.7%
C 98868
 
4.6%
Other values (28) 497034
23.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2128640
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
320967
15.1%
n 221542
10.4%
l 164596
 
7.7%
a 162209
 
7.6%
u 148913
 
7.0%
s 142875
 
6.7%
e 138751
 
6.5%
r 132470
 
6.2%
y 100415
 
4.7%
C 98868
 
4.6%
Other values (28) 497034
23.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2128640
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
320967
15.1%
n 221542
10.4%
l 164596
 
7.7%
a 162209
 
7.6%
u 148913
 
7.0%
s 142875
 
6.7%
e 138751
 
6.5%
r 132470
 
6.2%
y 100415
 
4.7%
C 98868
 
4.6%
Other values (28) 497034
23.3%

temp
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.722624
Minimum-4
Maximum50
Zeros35
Zeros (%)< 0.1%
Negative53
Negative (%)< 0.1%
Memory size1.9 MiB
2024-05-08T13:40:02.455772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile11
Q118
median24
Q331
95-th percentile40
Maximum50
Range54
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.8809133
Coefficient of variation (CV)0.35922212
Kurtosis-0.55427701
Mean24.722624
Median Absolute Deviation (MAD)6
Skewness0.13560896
Sum6156502
Variance78.870621
MonotonicityNot monotonic
2024-05-08T13:40:02.859626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 10432
 
4.2%
22 10238
 
4.1%
20 10013
 
4.0%
24 9952
 
4.0%
21 9939
 
4.0%
25 9835
 
3.9%
19 9628
 
3.9%
26 9552
 
3.8%
27 9420
 
3.8%
18 9154
 
3.7%
Other values (45) 150860
60.6%
ValueCountFrequency (%)
-4 1
 
< 0.1%
-3 7
 
< 0.1%
-2 7
 
< 0.1%
-1 38
 
< 0.1%
0 35
 
< 0.1%
1 67
 
< 0.1%
2 156
 
0.1%
3 250
 
0.1%
4 465
0.2%
5 731
0.3%
ValueCountFrequency (%)
50 2
 
< 0.1%
49 13
 
< 0.1%
48 33
 
< 0.1%
47 190
 
0.1%
46 603
 
0.2%
45 1222
0.5%
44 1728
0.7%
43 2146
0.9%
42 2651
1.1%
41 2886
1.2%

wind
Real number (ℝ)

ZEROS 

Distinct44
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.957104
Minimum-1
Maximum163
Zeros26711
Zeros (%)10.7%
Negative109
Negative (%)< 0.1%
Memory size1.9 MiB
2024-05-08T13:40:03.285397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q17
median11
Q319
95-th percentile30
Maximum163
Range164
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.7116195
Coefficient of variation (CV)0.67234308
Kurtosis1.1781924
Mean12.957104
Median Absolute Deviation (MAD)5
Skewness0.77252211
Sum3226617
Variance75.892314
MonotonicityNot monotonic
2024-05-08T13:40:03.724557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
7 31173
12.5%
0 26711
10.7%
11 25646
10.3%
9 22989
9.2%
15 22684
9.1%
19 20276
8.1%
6 16513
 
6.6%
13 15182
 
6.1%
22 13394
 
5.4%
17 10024
 
4.0%
Other values (34) 44431
17.8%
ValueCountFrequency (%)
-1 109
 
< 0.1%
0 26711
10.7%
2 402
 
0.2%
4 7849
 
3.2%
6 16513
6.6%
7 31173
12.5%
9 22989
9.2%
11 25646
10.3%
13 15182
6.1%
15 22684
9.1%
ValueCountFrequency (%)
163 1
 
< 0.1%
115 1
 
< 0.1%
93 2
 
< 0.1%
80 1
 
< 0.1%
76 1
 
< 0.1%
70 2
 
< 0.1%
67 5
< 0.1%
65 6
< 0.1%
63 2
 
< 0.1%
61 6
< 0.1%
Distinct92
Distinct (%)< 0.1%
Missing17
Missing (%)< 0.1%
Memory size1.9 MiB
2024-05-08T13:40:04.514489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9343269
Min length2

Characters and Unicode

Total characters730665
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row64%
2nd row64%
3rd row72%
4th row72%
5th row72%
ValueCountFrequency (%)
13 6903
 
2.8%
15 5878
 
2.4%
11 5858
 
2.4%
12 5466
 
2.2%
14 5370
 
2.2%
10 5261
 
2.1%
18 5183
 
2.1%
20 5110
 
2.1%
16 5054
 
2.0%
17 4869
 
2.0%
Other values (82) 194054
77.9%
2024-05-08T13:40:05.755174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
% 249006
34.1%
1 78216
 
10.7%
2 67351
 
9.2%
3 61845
 
8.5%
4 52297
 
7.2%
5 45630
 
6.2%
6 42676
 
5.8%
7 40641
 
5.6%
8 38743
 
5.3%
0 27809
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 730665
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
% 249006
34.1%
1 78216
 
10.7%
2 67351
 
9.2%
3 61845
 
8.5%
4 52297
 
7.2%
5 45630
 
6.2%
6 42676
 
5.8%
7 40641
 
5.6%
8 38743
 
5.3%
0 27809
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 730665
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
% 249006
34.1%
1 78216
 
10.7%
2 67351
 
9.2%
3 61845
 
8.5%
4 52297
 
7.2%
5 45630
 
6.2%
6 42676
 
5.8%
7 40641
 
5.6%
8 38743
 
5.3%
0 27809
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 730665
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
% 249006
34.1%
1 78216
 
10.7%
2 67351
 
9.2%
3 61845
 
8.5%
4 52297
 
7.2%
5 45630
 
6.2%
6 42676
 
5.8%
7 40641
 
5.6%
8 38743
 
5.3%
0 27809
 
3.8%

barometer
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)< 0.1%
Missing72
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1015.4554
Minimum904
Maximum1101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2024-05-08T13:40:06.215244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum904
5-th percentile1003
Q11011
median1016
Q31021
95-th percentile1025
Maximum1101
Range197
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.9707793
Coefficient of variation (CV)0.0068646832
Kurtosis0.078858563
Mean1015.4554
Median Absolute Deviation (MAD)5
Skewness-0.40685837
Sum2.5279863 × 108
Variance48.591764
MonotonicityNot monotonic
2024-05-08T13:40:06.723621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
1022 12779
 
5.1%
1016 12637
 
5.1%
1023 12612
 
5.1%
1015 12592
 
5.1%
1021 12511
 
5.0%
1014 12475
 
5.0%
1013 12298
 
4.9%
1024 12029
 
4.8%
1012 11585
 
4.7%
1017 11309
 
4.5%
Other values (36) 126124
50.6%
ValueCountFrequency (%)
904 1
 
< 0.1%
990 2
 
< 0.1%
991 29
 
< 0.1%
992 71
 
< 0.1%
993 224
 
0.1%
994 425
0.2%
995 526
0.2%
996 610
0.2%
997 678
0.3%
998 678
0.3%
ValueCountFrequency (%)
1101 2
 
< 0.1%
1053 1
 
< 0.1%
1032 2
 
< 0.1%
1031 44
 
< 0.1%
1030 138
 
0.1%
1029 495
 
0.2%
1028 1392
 
0.6%
1027 3052
 
1.2%
1026 6689
2.7%
1025 9501
3.8%

visibility
Real number (ℝ)

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.053453
Minimum-1
Maximum161
Zeros405
Zeros (%)0.2%
Negative49644
Negative (%)19.9%
Memory size1.9 MiB
2024-05-08T13:40:07.124530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q15
median16
Q316
95-th percentile16
Maximum161
Range162
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.053005
Coefficient of variation (CV)0.63808161
Kurtosis0.60703805
Mean11.053453
Median Absolute Deviation (MAD)0
Skewness-0.79262276
Sum2752564
Variance49.74488
MonotonicityNot monotonic
2024-05-08T13:40:07.502455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
16 160541
64.5%
-1 49644
 
19.9%
8 9679
 
3.9%
7 6621
 
2.7%
6 4479
 
1.8%
5 4358
 
1.8%
9 3877
 
1.6%
4 2877
 
1.2%
3 2654
 
1.1%
2 2420
 
1.0%
Other values (5) 1873
 
0.8%
ValueCountFrequency (%)
-1 49644
19.9%
0 405
 
0.2%
1 1462
 
0.6%
2 2420
 
1.0%
3 2654
 
1.1%
4 2877
 
1.2%
5 4358
 
1.8%
6 4479
 
1.8%
7 6621
 
2.7%
8 9679
 
3.9%
ValueCountFrequency (%)
161 2
 
< 0.1%
35 1
 
< 0.1%
29 3
 
< 0.1%
16 160541
64.5%
9 3877
 
1.6%
8 9679
 
3.9%
7 6621
 
2.7%
6 4479
 
1.8%
5 4358
 
1.8%
4 2877
 
1.2%

Interactions

2024-05-08T13:39:48.630783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:22.868514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:26.011521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:29.335254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:32.846207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:36.227340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:38.640086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:41.613249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:45.013786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:49.012195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:23.102452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:26.362677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:29.700827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:33.206340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:36.603268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:38.841745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:41.969880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:45.404463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:49.390827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:23.345309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:26.713748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:30.222784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:33.570848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:36.969960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:39.067827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:42.349213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:45.774089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:49.763919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:23.619093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:27.073435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:30.577343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:33.928531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:37.263106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:39.300087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:42.714701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:46.161638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:50.149375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:23.991838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:27.449160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:30.949361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:34.294161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:37.483520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:39.535587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:43.091010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:46.554533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:50.531478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:24.400030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:27.833145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:31.320677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:34.665458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:37.712283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:39.898512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:43.456675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:46.945713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:50.896541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:24.834474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:28.178241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:31.672855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:35.042930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:37.908914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:40.437621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:43.813258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:47.342496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:51.282021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:25.206168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:28.572338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:32.061329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:35.430540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:38.132846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:40.813962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:44.200505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:47.773210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:51.688195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:25.611875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:28.950897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:32.463411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:35.838040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:38.386461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:41.202624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:44.609154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-08T13:39:48.212392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-08T13:40:07.803768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 0barometercitydayhourminutemonthtempvisibilitywindyear
Unnamed: 01.0000.3810.8260.004-0.0040.0060.040-0.177-0.0170.0230.146
barometer0.3811.0000.313-0.003-0.025-0.021-0.072-0.683-0.018-0.1340.072
city0.8260.3131.0000.000-0.0040.0050.0150.0560.022-0.0710.091
day0.004-0.0030.0001.0000.001-0.0010.0130.023-0.0240.0140.013
hour-0.004-0.025-0.0040.0011.0000.010-0.0010.201-0.0780.2200.000
minute0.006-0.0210.005-0.0010.0101.000-0.005-0.007-0.0800.0510.015
month0.040-0.0720.0150.013-0.001-0.0051.0000.2630.025-0.0840.350
temp-0.177-0.6830.0560.0230.201-0.0070.2631.000-0.0080.2490.191
visibility-0.017-0.0180.022-0.024-0.078-0.0800.025-0.0081.000-0.2030.045
wind0.023-0.134-0.0710.0140.2200.051-0.0840.249-0.2031.0000.040
year0.1460.0720.0910.0130.0000.0150.3500.1910.0450.0401.000

Missing values

2024-05-08T13:39:52.242812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-08T13:39:53.237956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-08T13:39:54.136585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0citydatetimeyearmonthdayhourminuteweathertempwindhumiditybarometervisibility
00Qassim1 January 201700:00201711240Clear171164%1018.016
11Qassim1 January 201701:0020171110Clear17664%1018.016
22Qassim1 January 201703:0020171130Clear151172%1019.016
33Qassim1 January 201704:0020171140Clear151172%1019.016
44Qassim1 January 201705:0020171150Clear15972%1019.016
55Qassim1 January 201706:0020171160Clear131382%1019.016
66Qassim1 January 201707:0020171170Sunny12788%1019.016
77Qassim1 January 201708:0020171180Sunny14972%1021.016
88Qassim1 January 201709:0020171190Sunny15972%1021.07
99Qassim1 January 201710:00201711100Sunny17764%1021.09
Unnamed: 0citydatetimeyearmonthdayhourminuteweathertempwindhumiditybarometervisibility
2490132843Jawf30 April 201914:002019430140Partly sunny351914%1016.0-1
2490142844Jawf30 April 201915:002019430150Scattered clouds34914%1015.0-1
2490152845Jawf30 April 201916:002019430160Scattered clouds351512%1014.0-1
2490162846Jawf30 April 201917:002019430170Scattered clouds351912%1014.0-1
2490172847Jawf30 April 201918:002019430180Passing clouds341713%1014.0-1
2490182848Jawf30 April 201919:002019430190Passing clouds321914%1014.0-1
2490192849Jawf30 April 201920:002019430200Passing clouds29922%1015.0-1
2490202850Jawf30 April 201921:002019430210Passing clouds27724%1016.0-1
2490212851Jawf30 April 201922:002019430220Clear26026%1017.016
2490222852Jawf30 April 201923:002019430230Clear24729%1017.016